The existing literature fails to consider the properties of many real-world situations that require deidentification. Unlike in proper face datasets, faces in real-world applications are typically low-resolution and distanced from the camera with a lot of movement. For example, in clinical situations, the assessment is the focus; the recorded video is solely for reference. In some settings, the subjects are children, whose faces are known to have a negative facial recognition performance when compared to adults [1]}.
